Text Generation
Transformers
TensorBoard
Safetensors
biology
genomics
rna
sequence-generation
regression
reinforcement-learning
git-lfs
Instructions to use JoyXiangLab/rnaseek-full with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JoyXiangLab/rnaseek-full with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="JoyXiangLab/rnaseek-full")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("JoyXiangLab/rnaseek-full", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use JoyXiangLab/rnaseek-full with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "JoyXiangLab/rnaseek-full" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "JoyXiangLab/rnaseek-full", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/JoyXiangLab/rnaseek-full
- SGLang
How to use JoyXiangLab/rnaseek-full with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "JoyXiangLab/rnaseek-full" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "JoyXiangLab/rnaseek-full", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "JoyXiangLab/rnaseek-full" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "JoyXiangLab/rnaseek-full", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use JoyXiangLab/rnaseek-full with Docker Model Runner:
docker model run hf.co/JoyXiangLab/rnaseek-full
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"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"gpuType": "V100"
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"source": [
"Clone ExLlamaV2 and install dependencies"
],
"metadata": {
"id": "NTOhV8supsTA"
}
},
{
"cell_type": "code",
"source": [
"!git clone https://github.com/turboderp/exllamav2\n",
"!cd exllamav2 && pip install -r requirements.txt"
],
"metadata": {
"id": "MkEIIMJdpk_d"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Optional: install Flash Attention"
],
"metadata": {
"id": "Wv96vR6HpNZF"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "KjOm-GNZpMA0"
},
"outputs": [],
"source": [
"!pip install -U flash-attn"
]
},
{
"cell_type": "markdown",
"source": [
"Download a model. This may take a moment."
],
"metadata": {
"id": "MqAu9pcBqAf4"
}
},
{
"cell_type": "code",
"source": [
"!mkdir my_model\n",
"!huggingface-cli download turboderp/Mistral-7B-instruct-exl2 --revision 4.0bpw --local-dir my_model"
],
"metadata": {
"id": "4OotLYL3p7rD"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Launch the chatbot example. On the first launch, this will compile ExLlamaV2's C++/CUDA extension, which can take several minutes on Colab."
],
"metadata": {
"id": "4DcCw_URrHja"
}
},
{
"cell_type": "code",
"source": [
"!cd exllamav2 && python examples/chat.py -m ../my_model -mode llama -pt -ncf -ngram"
],
"metadata": {
"id": "HbpsCnOoqKzk"
},
"execution_count": null,
"outputs": []
}
]
}
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